Generally, how to scale different variables when aggregating them in a
dissimilarity measure is strongly dependent on the subject matter, what the
aim of clustering and your "cluster comncept" is. This cannot be answered
properly on such a mailing list.
A standard transformation before computing dissimilarities would be to
scale all variables to variance 1 by dividing by their standard deviations.
This gives in some well defined sense all
variables the same weight (which may be somewhat affected by
outliers, heavy tails, skewness; note, however, that normalising to the same
range shares the same problems more severly).
Regards,
Christian
On Mon, 26 Jan 2009, mau...@alice.it wrote:
I am going to try out a tentative clustering of some feature vectors.
The range of values spanned by the three items making up the features vector is
quite different:
Item-1 goes roughly from 70 to 525 (integer numbers only)
Item-2 is in-between 0 and 1 (all real numbers between 0 and 1)
Item-3 goes from 1 to 10 (integer numbers only)
In order to spread out Item-2 even further I might try to replace Item-2 with
Log10(Item-2).
My concern is that, regardless the distance measure used, the item whose order
of magnitude is the highest may carry the highest weight in the process of
calculating the similarity matrix therefore fading out the influence of the
items with smaller variation in the resulting clusters.
Should I normalize all feature vector elements to 1 in advance of generating
the similarity matrix ?
Thank you so much.
Maura
tutti i telefonini TIM!
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*** --- ***
Christian Hennig
University College London, Department of Statistical Science
Gower St., London WC1E 6BT, phone +44 207 679 1698
chr...@stats.ucl.ac.uk, www.homepages.ucl.ac.uk/~ucakche
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